1

HOMEWARD BOUND FDI: ARE MIGRANTS A BRIDGE OVER

TROUBLED FINANCE?

Cuadros, A., Martín-Montaner, J. and Paniagua, J.

Preliminary draft, please do not quote

This version, 4th August 2014

Abstract

Information barriers have been at the core of the debate on international capital flows.

Foreign Direct Investment (FDI) has been substantially more sensitive to information

frictions than investment in portfolio equity and debt securities (Daude and Fratzscher,

2008). Migrants can lower these barriers providing information to investors about their

country or origin as well as reducing transactions costs by sharing their expertise about

regulation, customs and procedures. In fact, the potential impact of migrants on FDI is

expected to be higher than on trade (see Tong, 2006 and Kugler and Rapoport, 2011).

This paper shed new light into the role played by migrants as a driver of FDI flows.

Unlike most existing studies, which take total capital flows as the main dependent

variable, we focus on both the number of investments abroad and the scale of the

projects undertaken (extensive and intensive margins, respectively). Moreover, we

consider migrants as financial entrepreneurs that may ease credit constraints faced by

foreign investors after the 2007 financial crisis. Our findings for a large number of

developed and developing countries during the period 2003-2012 support that the

impact of immigrants comes mainly through their influence on the number of outward

FDI projects undertaken (extensive margin). Also, our results shed new light regarding

the role played by migrant as financial entrepreneurs who may ease the access to credit

through their networks.

JEL Clasification: F22, F23, F16

Keywords: migration, FDI, foreign employment, financial constraints.

Acknowlegments:

Authors gratefully acknowledge financial support from the Spanish Ministerio de

Economía y Competitividad and FEDER (ECO2011-28155), the Spanish Ministerio de

Educación (ECO2011-27619) and the Pla de Promoció de la Investigació de la

Universitat Jaume I (P1.1B2013-22).

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1. Introduction

An extensive body of research has documented a positive association between ethnic

networks and international trade since the seminar work of Gould (1994). Most of this

literature has focused on the so-called network channel, that is, the fact that migrants

display useful knowledge about their home countries which helps exporters to reduce

transaction costs and therefore enhances bilateral trade.

However, the link between migrants and Foreign Direct Investment (FDI) remains

relatively unexplored. This is surprising as FDI activities face larger information

asymmetries than international trade transactions, thus we would expect networks to

play a more important role in bilateral FDI than in trade (see Tong, 2006). Kugler and

Rapoport (2011) support this hypothesis by investigating the relationship between trade,

migration and FDI. These authors conclude that, to the extent that international

transactions are facilitated by the information transmitted by migrants, the impact is

stronger on FDI than on trade. The influence of cultural factors on FDI seems to be also

particularly likely as this exchange no only involves a transfer of foreign capital but also

a lasting interest in an acquired company (see Bandelj, 2002). As emphasized by

Javorcik et al (2011), FDI implies a long-term investment and therefore requires a wider

variety of information about the market, legal framework, and business structure in the

host country. This long-term nature explains why some authors consider FDI flows to

be more sensitive to information frictions than investment portfolio equity and debt

securities (Daude and Fratzscher, 2008). In fact, new approaches in the literature stress

that uncertainty is an important barrier to multinational corporations’ investment and

suggest a complementary relationship between capital and labor flows (Buch et al,

2006).

Unlike most previous studies, that take total capital flows as their main dependent

variable, we focus on both the scale as well as the number of FDI projects undertaken

(intensive and extensive margins, respectively). Under this perspective we assume, on

the one hand, that the effect of migrants on these two variables may be different. This

could happen if, for example, the information provided by migrants affect the number of

investment projects but not the total amount of investment. In this sense, migrants may

act as transnational entrepreneurs acting as business people who create new economic

opportunities in the host economy. This can happen through the existence of personal

ties between expatriates and business members of host countries that can influence

investment decisions as affiliates to host locations facilitate information flows and/or

lobby for certain locations as opposed to others (see Bandelj, 2002). In other words,

foreign employees may act as knowledge brokers that transfer knowledge from where it

is known to where it is not (Paniagua and Sapena, 2013 and Bergstrand et al, 2008). On

the other hand and, taking into account the likely potential impact of financial

constraints on potential entrepreneurs (Alfaro, 2004), we examining the role played by

3

migrants in easing credit constraints faced by foreign investors after the 2007 financial

crisis. Migrants can act as financial entrepreneurs by easing the access to credit through

their networks. Thus, when the traditional credit channels fail, financial entrepreneurs

step in to provide the credit not provided by the banking system. Moreover, as stated by

Gil Pareja et al (2013), credit constraints derived from system banking crises affect

primarily FDI’s extensive margin.

Although the above arguments seem be applicable mainly to skilled workers, there other

channels through which unskilled workers may also contribute to relax information

constraints on FDI. As an example, participation in the destination country’s labor force

may reveal information about the characteristics of workers in migrant home country,

providing valuable information for FDI projects. Therefore, both skilled and unskilled

migration can provide information to facilitate FDI (see Kugler and Rapoport, 2011).

The reminder of this paper is as follows. Section 2 reviews the relevant literature.

Section 3 presents the theoretical model. Section 4 presents the empirical methodology

and data. Section 5 describes the results and Section 6 concludes.

2. Related literature/Migration FDI literature

New approaches in the literature stress that uncertainty as well as information are

important barriers to both trade and multinational corporations’ investment and suggest

a complementary relationship between trade, capital and labor flows (see Kugler and

Rapoport, 2011 and Buch et al, 2006). These approaches, based on the theory of

networks, suggest that both emigrants and immigrants are expected to have positive and

significant effects on bilateral trade and FDI flows. However, whereas the relationship

between migration and trade has been extensively analyzed, the FDI-migration link

remains relatively unexplored.

FDI and migration may interact in many ways. Acting as an “information revealing”

network, migrant workers may stimulate FDI inflows in their origin country. This could

happen, for example, when people living abroad demand products or services from their

home country and companies who identify these needs try to satisfy them by investing

abroad. Emigrants can also broad their knowledge and skills through working abroad

and acquire capacities to realize business activities in their home country (Brain gain).

In fact, by acquiring skills during their stay abroad, returning migrants may contribute

to the spread of technological progress in their home country (see Federici and

Giannetti, 2010)1. Thus, it is also reasonable to expect that economic performance of

host countries may improve through the presence of multinational enterprises and the

new technology and know-how that they may bring with them2, which may lower the

1 These authors support the hypothesis that the brain drain associated with migration does not always

apply. 2 See Borenzstein et al (1998) and Alguacil et al (2004.

4

incentives to migrate. The company may also bring its executives from the home

country to ensure that the subsidiary maintains quality and product standards or it may

employ countrymen living or lived abroad. This practice would cause a migration

towards the host country of FDI.

In fact, quite a few studies provide evidence in favor of the hypothesis that capital and

labor often move in the same direction. Most of them are individual case studies that

analyze both inward and outward FDI and concentrate mainly on immigrant’s networks.

The United States experience has been widely analyzed. Results obtained by Javorcik et

al (2011) indicate that outward US FDI is positively related with the presence of

migrants in the United States, being this relationship stronger for migrants with tertiary

education. Kugler and Rapoport (2007) report a contemporaneous substitutability but a

dynamic complementarity between immigration and outward FDI in this country. Thus,

immigrants provide information about future investment opportunities in their country

of origin. More recently, Bhattacharya et al (2008) find that the size of the immigrants

group from a country living in the US is positively related with US investment in that

country. Foad (2012) adopts a regional perspective and looks at the regional distribution

of both FDI and immigration from 10 source countries to the 50 US states. The results

of this paper indicate that the presence of an immigrant community leads to new FDI

from those immigrants native countries.

The Chinese case has also received attention by some empirical studies. That is the case

of Tong (2005) that accounts by the Chinese networks by the product of the number of

Ethnic Chines in the source and the host countries and report evidence about a

significant positive influence of these networks on bilateral FDI. Similarly, Gao (2003)

finds a positive impact of ethnic Chinese networks (defined as the population share of

ethnic Chinese in the investing country) and inward FDI.

Other countries experiences have also been analyzed. That is the case of Buch et al

(2006) concluding that there are higher stocks of inward FDI in German states hosting a

large foreign population from the same country of origin. Gheasi et al (2013) show that

UK investment abroad is positively related to the presence of immigrants. Finally,

Murat and Pistoresi (2009) report that networks of Italian emigrants abroad promote

both inward and outward FDI although the evidence for immigrants is weaker.

According to these authors, the significant impact or Italian “diasporas” may be

explained by the small average size of the Italian firms which makes them particularly

dependent on personal contacts for their transactions and investment abroad.

To the best of our knowledge, the number of multicountry approaches is considerably

smaller. Docquier and Lodigiani (2010) report strong networks externalities for a

sample of 150 countries during the period 1980-2000, mainly associated with the skilled

diaspora. These authors do not use bilateral data but aggregate stock of FDI-funded

capital received by world countries. Flisi and Murat (2011) study the relationship

between bilateral FDIs and immigrant networks in France, Germany, UK, Italy and

5

Spain and the emigrant Diaspora just for Italy and Spain. Their findings indicate that

FDI in UK, Germany and France are prompted by the ties of skilled immigrants, while

those of Italy and Spain are only influenced by their respective emigrant Diaspora

(although they cannot separate skilled and unskilled workers within the diaspora). In

these two last countries, immigrants have weak or nil effect. According to the authors,

this disparity may arise from the past history of international migration in the two

groups of countries: one more based on colonialism, the other on labor migration. Also,

because (unlike France, Germany and UK) Italy and Spain have built and still maintain

close links with their emigrant diasporas. Kugler and Rapoport (2011) find that

migration positively affects both trade and FDI (at both the extensive and intensive

margins), but the impact on the latter is higher. Their analyses cover a sample of 203

countries for the average period 2001-2006. Ivels and De Mello (2010) also analyze

trade, FDI and migration in a unified framework from the perspective of migration-

sending countries. Their results indicate that if exports are low-skill intensive,

emigration of high-skill labor leads to positive FDI.

Most of the above referred studies do not identify the specific channels through which

the information carried by migrants helps to lead more FDI to their countries of origin.

Recent research, however, has highlighted the role of immigrants as business developers

(Foley and Kerr, 2013), that is, individuals of a certain ethnicity that possess specific

knowledge about how to conduct business in countries associated with that ethnicity.

Under this approach, the particularities of the entrepreneur reside in ethnic resources. It

is the access to this type of resources that allows the entrepreneur to initiate, finance and

develop their business. The relations of trust and friendship that entrepreneurs maintain

with others from the same ethnic background through social networks are the origin of

such resources (see Rueda-Armengot and Peris-Ortiz, 2012),3 which can be materialized

not only in or intangible aspects (information, orientation, advice) but also in tangible

ones such as financing. In fact, if migrants integrate into a business community, a

network can emerge whereby migrants liaise between potential investors and partners

(Kugler and Rapoport, 2011). We are interested in this latter aspect, as allows relating

migrant networks to a key issue for potential foreign investors: access to financial

markets in the host country.

Financial markets are crucial for FDI flows: deep financial markets provide firms’

access to the capital needed to undertake investment projects (Di Giovanni, 2005).

Conversely, its absence may constraint potential entrepreneurs, as stressed by Alfaro et

al. (2004). In this sense, Kroszner et al. (2007) achieve two important results: first,

value added in those sectors with higher dependence of external finance contracts

during a banking crisis; second, those contractions are substantially greater in countries

with more evolved financial markets. Bruch et al. (2014) achieve similar results:

3 See also the work by Saxenian (2002) on American transnational entrepreneurs.

6

financial constraints affect more deeply larger and/or more productive firms which are

more likely to engage into FDI decisions. Consequently, access to external finance can

be a crucial determinant of business expansion. As highlighted by Héricourt and Poncet

(2009), the development of cross-border relationships between Chinese and foreign firm

helps private domestic firms to bypass both the financial and legal obstacles that they

face at home. Foreign firms are expected to face a lower degree of financial constraints

compared with their domestic counterparts (Guariglia and Mateut, 2010).

The purpose of the present research is to link, if possible, the two bodies of literature

reviewed in the paragraphs above. First, we take as a starting point the high sensitivity

of FDI to information costs and examine the role played by migrant networks in easing

them. Second, we analyse whether access to credit markets is one of the channels

through which the previous mechanism works. In fact, migrants could find interesting to

help inward FDI in their countries of origin. Harrison et al. (2004) found that FDI

inflows reduce financial constraints of domestic owned enterprises, so that their

investments become less sensible to cash. Besides these effects appear to be stronger for

low-income (and, therefore, more likely origin of migration flows) than for high-income

regions.

In order to motivate this strategy, we must take into account that the credit constraints

following the financial crisis of 2007 constitute a particularly relevant environment for

our analysis. Thus, Campello et al. (2010) observe that the systemic banking crisis since

2007 forced most of the financially constrained firms to drop investment projects

(whereas unconstrained firms didn’t). Prior to the crisis, Alba et al. (2007), focusing on

the case of Japanese FDI into the U.S., had already found that multiple rating

downgrades of Japanese banks significantly affects the rate of FDI in those firms which

have the downgraded banks as their main financial source. Through the consideration of

the credit shortage that have followed the financial crisis of 2007, we may wonder

whether the effectiveness of migrant networks in promoting FDI been conditioned by

these constraints.

3. The model

The model considers a firm producing and selling products in country i. The revenue to

be a strictly increasing and concave function of the quantity produced in that country .

The concavity in the revenue function may stem from technology, or market

preferences. The quantity produced is, therefore, assumed to be a CES Cobb-Douglas

type:

⁄

⁄ [1]

7

where L is labor; K is the capital stock, are the wages a i and the interest rate and

is sector wide parameter that describes the intensity in which each factor is

used in the production of .

In this setup, the maximization function faced by the enterprise is given by:

{

⁄

⁄ } [2]

where are the prices at which it sells at country i and is a fixed cost of production.

Foreign production

Consider now that the firm plans to setup a similar plant in country j. We assume that

the company uses credit channels (formal and informal) at the home country to finance

its foreign operations. Financial frictions occur as a problem of limited commitment

(Antràs & Foley, forthcoming; Paniagua & Sapena, 2014a; Thomas & Worrall, 1994).

As a result of systemic banking crises domestic banks at country i may not abide fully

by the financial contract. When the contract is not enforced, the bank does not stand by

the initial terms of the contract with the firm. In particular, the contract is enforced with

probability , where is an index of the financial quality of country i.

For simplicity, we assume a horizontal integration of the MNE, that is, the fixed costs

from this new facility are the same that the existing plant with . The expected

revenue will diminish by a fraction which captures the extent of banking

contractual frictions. However, in this case the MNE can procure capital outside the

banking system independently from the systemic banking shocks. We assume that its

ability to do so depends on the immigrant network of the host country j present in the

source country i. When the traditional credit channels fail, financial entrepreneurs step

in to provide the credit that banks do not provide (Alfaro, Chanda, Kalemli-Ozcan, &

Sayek, 2004). These migrant financial networks may also contribute with knowledge of

the host country preferences, financial or legal system.

Therefore the production constraint of the MNE results in:

(

⁄ )

( ⁄ )

[3]

where represents the extent by which the MNE may procure capital outside the

banking system. Equation [3] reads that with a probability , the bank is not

abiding by the contract with the MNE and expected employment is reduced by .

However, for capital intensive plants ( , credit constraints are offset by migrant

financial networks outside the banking system. The model does not delve into the ways

the immigrants secure the credit, they may do so through their savings or via credit from

their country of origin (j), when source and host country do not suffer from

8

contemporaneous banking crises. In this last case, immigrants act as financial and

knowledge brokers between both parties (Paniagua & Sapena, 2013).

The expected revenue of the MNE in the host country results in:

{ (

⁄ )

( ⁄ )

} [4]

Applying the envelope theorem to expressions [2] and [4], the MNE prefers investing in

country j over domestic production if and only if

(

⁄ )

(

⁄ )

[5]

From [5], these conclusions follow: The likelihood that a greenfield investment occurs

in country j as opposed to home country i is governed by the relative magnitude of the

wages ( , prices the financial quality at country i and the immigrant

network . It is increasing in the financial stability at home ( , the relative prices

and in the and the immigrant flows.

4. Empirical methodology and data.

We estimate an extended gravity equation which includes the number of migrants to

explain bilateral FDI from the host country of immigrants to their country of origin.

Thus, this is our basic specification:

As it is usual in the gravity framework, the extent of FDI flows between country pairs

is directly proportional to their economic mass (i.e., gross domestic product, GDP) and

decreases with distance, a proxy for freight costs, and other factors that affect cross

border investments. All the variables measuring these latter factors are detailed in Table

1.

This basic specification is enlarged to include the number of migrants and the existence

of a systemic financial crisis. Our baseline specification is the following augmented

gravity equation:

( ) ( )

( )

,

[6]

9

where is the aggregate investment between home country i and host j in year t; Y

denotes the domestic gross product (GDP), is yearly stock of migrants from

country j who live in country i; is dummy set to one if the country suffers a systemic

banking crisis in year t; is a fixed year effect; and lastly represent an stochastic

error term.

Table 1. Gravity variables

Gravity variables without time variation

Logarithm of distance in kilometers between country capitals

borderij Takes the value 1 when countries share a common border, and 0

otherwise

colij Takes the value 1 if the two countries have ever had a colonial link,

and 0 otherwise

langij Takes a positive value if both countries share the same official

language

relij Is a composite index that measures the religious affinity between

country pairs with values ranging from 0 to 1

smctryij Is an indicator variable that indicates if both countries were part of the

same country in the past

lockedj Is 1 if the host country is landlocked

Gravity variables with time variation

BITijt Is a dummy that takes a value of one if the country pair has a bilateral

investment treaty in force

FTAijt Is a dummy that indicates whether both countries have a free trade

agreement in force

The plain ordinary least squares (OLS) estimation of the gravity equation [6] suffers

from several well-known biases. Firstly, there is a mis-specification due to the omission

of multilateral resistance terms (Anderson and Van Wincoop's, 2003). The usual

solution implies introduce country fixed effects (CFE) for both host and supply

countries. Moreover, CFE does not eliminate completely the unobserved bilateral

heterogeneity owning to ignoring other variables at the country pair level that might

affect bilateral FDI. However, country-pair fixed effects (CPFE) eliminate all dyadic

variables with no time variation plus distance. In particular, the CPFE compromises

only GDP, BIT, FTA and migration.

10

Secondly, the log version of the gravity equation incurs in a self-selection bias which

stems from the omission of zeros. This problem has been solved the Pseudo-Poisson

Maximum likelihood (PPML) estimator proposed by Silva and Tenreyro (2006), which

presents consistent estimates since this estimator does not require a log-linearization of

the variables.4 In particular, we use the following specification:

(

( ) ( )

( ) ( ) ( )

)

[7]

Additionally to the total value of FDI, we also introduce as a dependent variable the

number of FDI projects. Gil Pareja et al. (2013) focus on FDI decisions and confirm this

significant negative impact on the number of projects undertaken investment decision,

but not on the amount invested by foreign firms. Since the initial cost of foreign

production (e.g., constructing a manufacturing plant) is relatively constant, under credit

constraints, firms place less rather than smaller bets.

Besides, the incorporation of trade and FDI margins reduces an over aggregation bias of

capital flows in the estimation of the gravity equation (Hillberry & Hummels, 2008;

Hillberry, 2002) and therefore, it is an specification more closely grounded in theory.

Additionally, it reveals information on existing links on the creation of new partners

(Felbermayr & Kohler, 2006).

5. Data

The Financial Times Ltd. cross-border investment monitor (FDIMarkets, 2013) is the

source of the FDI dataset. Investment counts are measured in firm level projects counts

and capital flows in constant 2005 USD. The dataset covers bilateral firm-level

greenfield investments from 2003 to 2012, aggregated between 190 countries. However,

4 Moreover, it is robust to heteroskadicity in the error term (Silva and Tenreyro, 2010) and it assures

converge of the maximum likelihood estimation by a previous inspection of the data (Silva & Tenreyro,

2011). Additionally, Bergstrand et al. (2013) argue that the PPML estimator is appropriate for short panel

gravity data.

11

this country size is reduced only to those countries from which we can obtain

immigration data, which comes from the OECD.

Immigration may also affect other types of FDI. However, greenfield investments incur

in higher plant costs and have a are prone to suffer from credit constraints (Gil-Pareja et

al., 2013; Nocke & Yeaple, 2007; Qiu & Wang, 2011). Consequently, greenfield

investments are optimal to measure the influence to immigrant networks in the ability of

MNEs to procure credit. Overall, the database is heavily unbalanced with 70% zero

observations, meaning that not all countries received investment in all years.

The World Bank (2013) is the source of GDP, measured in constant 2005 US dollars.

Distance, common language, colony and border come from the CEPII (2011) database

and control for freight, information, cultural, historic and administrative transaction

costs between country pairs. Religious affinities increases the probability of economic

transactions between nations with similar values and beliefs (Helble, 2007). The

variable religion was introduced in the gravity equation by Helpman, Melitz, and

Rubinstein (2008) as a control variable for religious and common law affinities between

trade partners. It is calculated with data from CIA World Factbook (2011) according to

following formula for country each country pair: %Christiani*%Christianj +

%Muslimi*%Muslimj + %Buddhisti*%Buddhistj + %Hindui*%Hinduj +

%Jewishi*%Jewishj. Institutional agreements such as Free Trade Agreements and

Bilateral Investments Treaties reduce the uncertainty in foreign investments (Bergstrand

& Egger, 2013). BIT is manually constructed with data from UNCTAD (2013). The

source of FTA is Head, Mayer, and Ries (2010) complimented UNCTAD (2013) data.

The source of systemic banking crises is Laeven and Valencia (2013). These authors

build up a database encompassing the period 1970-2011. They identify banking crises

as those events which simultaneously verify the following two conditions:

1. Significant signs of financial distress in the banking system (significant bank

runs, losses in the banking system, and/or bank liquidations)

2. Significant banking policy intervention measures in response to significant

losses in the banking system. 5

5 These measures should include at least three out of the following six: (1) Deposit freezes and/or bank

holidays; (2) significant bank nationalizations; (3) banks restructuring gross costs; (4) extensive liquidity

support; (5) significant guarantees put in place and (6) significant asset purchases.

12

6. Results and discussion

First, we present in Table 2 our results for all the econometrical models enumerated

above using as dependent variable total FDI. Columns (1) and (2) display, respectively,

the estimates for the OLSQ and PPML models with country fixed effects. GDP is not

significant in either model. Distance, common language and a former colony

relationship are significant and show the expected sign (also landlocked, although only

in the PPML model). However, sharing a border or religion or a common past as the

same country do not have any effect on the volume of investment. Bilateral treaties

display an unexpected negative effect on FDI, although free trade treaties are significant

only in the OLS estimation.

Table 2. Dependent Variable: Total FDI

OLS-

CFE

PPML-

CFE

OLS-

CPFE

PML-

CPFE

OLS-

CFE

PPML-

CFE

OLS-

CPFE

PML-

CPFE

( ) 0.011

(0.446)

-0.116

(0.379)

0.089

(0.380)

-0.075

(0.394)

0.041

(0.447)

-0.069

(0.376)

0.136

(0.382)

0.344*

(0.178)

( ) -0.596***

(0.092)

-0.448***

(0.106)

-0.551***

(0.093)

-0.437***

(0.106)

-0.386**

(0.174)

-0.104

(0.156)

-0.340

(0.289)

-0.044

(0.123)

-0.414**

(0.176)

-0.126

(0.159)

-0.333

(0.289)

0.091

(0.087)

-0.457***

(0.126)

-0.531***

(0.158)

-0.801**

(0.332)

-1.107**

(0.494)

-0.429***

(0.126)

-0.507***

(0.154)

-0.821**

(0.332)

-0.009

(0.141)

0.057

(0.219)

-0.126

(0.347)

0.025

(0.216)

-0.192

(0.340)

0.456**

(0.177)

0.478***

(0.160)

0.442**

(0.179)

0.446***

(0.155)

0.733***

(0.194)

0.966***

(0.188)

0.795***

(0.194)

0.999***

(0.183)

-0.349

(0.336)

-0.139

(0.445)

-0.340

(0.350)

-0.109

(0.463)

0.071

(0.403)

0.133

(0.451)

0.064

(0.404)

0.127

(0.447)

-0.047

(0.116)

-0.306*

(0.160)

-0.051

(0.117)

-0.313*

(0.162)

( ) 0.119*** (0.039)

0.202*** (0.039)

0.168 (0.192)

0.172 (0.266)

0.118*** (0.039)

0.210*** (0.039)

0.157 (0.192)

0.095 (0.084)

- - - - 0.036

(0.094)

0.126

(0.088)

0.119

(0.092)

0.024

(0.033)

Observations 3148 6557 3148 6370 3148 6557 3148 6370

R2 0.410 0.679 0.024 0.409 0.683 0.025

Country FE Yes Yes No No Yes Yes No No

Country pair

FE

No No Yes Yes No No Yes Yes

Robust standard errors in parentheses

* p < 0.10, ** p < 0.05, *** p < 0.01

13

With regard to our variable of interest, we observe that the presence of immigrants has a

positive impact on the volume of investment from the receiving country of migrants to

the source country. The impact is higher in the PPML estimation, as expected [quote].

On the other hand, the inclusion of the variable GR has no consequences. More

relevant is the substitution of country-fixed effects by country pair fixed effects; first,

the dyadic variables are dropped. Second, bilateral investment treaties show a

significant effect only in the OLS estimation, whereas in the PPML estimation depends

on the GR variable not being included. Third, GDP’s product is significant in the PPML

estimation if the GR variable is included. However, the more important variation is that

the number of migrants becomes not significant in all cases.

Table 3. Dependent Variable: Number of FDI projects

OLS-

CFE

PPML-

CFE

OLS-

CPFE

PML-

CPFE

OLS-

CFE

PPML-

CFE

OLS-

CPFE

PML-

CPFE

( ) 0.095

(0.171)

-0.432

(0.317)

0.101

(0.139)

-0.468

(0.312)

0.122

(0.171)

-0.443

(0.317)

0.134

(0.140)

-0.474

(0.314)

( ) -0.294***

(0.051)

-0.445***

(0.069)

-0.281***

(0.052)

-0.428***

(0.069)

-0.201**

(0.105)

-0.091

(0.092)

0.096

(0.106)

0.211*

(0.119)

-0.221**

(0.107)

-0.114

(0.097)

0.101

(0.106)

0.209*

(0.119)

-0.190***

(0.072)

0.035

(0.076)

0.236*

(0.121)

0.306

(0.192)

-0.170**

(0.072)

0.066

(0.076)

0.222*

(0.121)

0.316

(0.197)

0.036

(0.136)

-0.416*

(0.224)

0.014

(0.134)

-0.470**

(0.227)

0.385***

(0.119)

0.683***

(0.094)

0.375***

(0.119)

0.649***

(0.094)

0.420***

(0.130)

0.604***

(0.125)

0.464***

(0.131)

0.650***

(0.123)

0.130

(0.223)

0.340

(0.363)

0.137

(0.237)

0.364

(0.393)

-0.164

(0.201)

-0.183

(0.279)

-0.170

(0.203)

-0.175

(0.278)

-0.011

(0.061)

-0.107

(0.115)

-0.014

(0.061)

-0.127

(0.117)

( ) 0.081***

(0.025)

0.182***

(0.022)

-0.024

(0.070)

0.243**

(0.123)

0.080***

(0.024)

0.189***

(0.023)

-0.032

(0.070)

0.238*

(0.123)

0.046 (0.038)

-0.017 (0.041)

0.084** (0.033)

-0.021 (0.041)

Observations 3148 6557 3148 6370 3148 6557 3148 6370

R2 0.663 0.937 0.084 0.661 0.937 0.085

Country FE Yes Yes No No Yes Yes No No

Country pair

FE

No No Yes Yes No No Yes Yes

Robust standard errors in parentheses

* p < 0.10, ** p < 0.05, *** p < 0.01

In Table 3, we have used as a dependent variable the number of investment projects

between home country i and host j in year t. In this case, the gravity variables more or

14

less display the same results as in the estimations using total FDI (border is significant

now), and the treaties variables have a positive effect on the number of projects once

country pair effects are included. Plus, the number of immigrants is significant in all

cases but the OLS estimation with country pair fixed effects. However, the dummy GR

still remains not significant in most equations (it only displays an unexpected positive

effect in the OLS estimation including country pair fixed effects).

The key point in our approach is to identify whether the existence of systemic financial

crises affect the ability of migrants to help FDI from their countries of residence to their

countries of origin. Thus, what we are going to do next is to introduce an interaction

between the number of immigrants and the GR variable. The outcome is presented in

Table 4, where we show just the results for our variables of interests (the remaining

estimations are available upon request).

Table 4. Dependent variable: Total FDI and number of FDI projects

Total FDI Number of projects

OLS-

CFE

PPML-

CFE

OLS-

CPFE

PML-

CPFE

OLS-

CFE

PPML-

CFE

OLS-

CPFE

PML-

CPFE

( ) 0.099**

(0.041)

0.221***

(0.043)

0.140

(0.192)

0.087

(0.085)

0.067***

(0.024)

0.196***

(0.023)

-0.032

(0.071)

0.219*

(0.125)

-0.657

(0.413)

0.491

(0.307)

0.260

(0.379)

0.364

(0.798)

-0.469***

(0.162)

0.249

(0.191)

-0.119

(0.139)

0.298

(0.183)

( )

0.078**

(0.038)

-0.028

(0.025)

-0.002

(0.035)

0.009

(0.012)

0.053***

(0.015)

-0.017

(0.016)

0.0225*

(0.013)

-0.022

(0.016)

( )

0.046 (0.042)

-0.045 (0.034)

-0.044 (0.041)

-0.031 (0.072)

0.0472*** (0.017)

-0.036* (0.020)

0.013 (0.015)

-0.041** (0.019)

Observations 3148 6557 3148 6370 3148 6557 3148 6370

R2 0.410 0.687 0.027 0.662 0.938 0.087

Country FE Yes Yes No No Yes Yes No No

Country pair

FE

No No Yes Yes No No Yes Yes

Robust standard errors in parentheses

* p < 0.10, ** p < 0.05, *** p < 0.01

In this case, we observe in the OLS estimations that the existence of systemic financial

crisis in the host country makes more useful the information provided by immigrants,

both for total FDI and the number of projects which are approved by the investing

firms. Besides, the PPML estimations show that the knowledge of immigrants have

minor impact if the systemic crisis happens in the origin country.

15

For the sake of robustness, we replicate the previous estimations using as a dependent

variable the number of jobs that MNE create on the host country6 (Paniagua and

Sapena, 2014). MNE transfer human capital and knowledge from the home to the host

country and they play an important role in the host’s countries economy (Paniagua &

Sapena, 2013). Although several empirical studies have plugged employment data into

the gravity equation (Griffith, 2007), Paniagua and Sapena (2014) develop a model

which explains the effect of credit constraints on foreign direct employment using the

gravity model. In so forth, the estimation of the effect of immigration and credit

constraints on foreign jobs using the gravity equation is grounded in theory. They

conclude that the Foreign Direct Employment (FDE) created by multinationals is

constrained by financial frictions at the home country.

Table 5. Dependent variable: Number of Jobs

OLS-CFE PPML-CFE OLS-CPFE PML-CPFE

( ) 0.128***

(0.037)

0.299***

(0.036)

0.062

(0.162)

0.204

(0.202)

-0.603 (0.413)

0.445 (0.372)

0.082 (0.029)

0.364 (0.798)

( ) 0.065*

(0.034)

-0.044

(0.030)

0.001

(0.029)

-0.051

(0.030)

( ) 0.041 (0.039)

-0.057 (0.042)

-0.025 (0.035)

-0.066 (0.042)

Observations 3148 6557 3148 6370

R2 0.508 0.869 0.033

Country FE Yes Yes No No

Country pair FE No No Yes Yes

Robust standard errors in parentheses

* p < 0.10, ** p < 0.05, *** p < 0.01

In terms of significance, the results are quite similar to those achieved for the total

amount of FDI as displayed in Table 4. The existence of systemic crisis in the country

of origin of the investment increases the impact of migrants.

One of the main issues concerning the estimation of FDI data is the endogeneity bias

(Aisbett, 2009; Bergstrand & Egger, 2013). To combat the endogeneity, we use the

Generalized Method of Moments (GMM) estimator. The GMM performs two

simultaneous estimations: one in levels with lagged first differences as instruments, and

one in first differences with lagged levels as instruments. In particular, we use the

system-GMM, which is appropriate for linear dynamic panel-data CPFE models

(Arellano and Bond, 1991; Blundell and Bond, 1998). Additionally system-GMM takes

6 Jobs are the individual employees hired at the host by the MNE (FDIMarkets, 2013).

16

care of other potentially endogenous variables (Busse, Königer, & Nunnenkamp, 2010),

for instance, BIT in our equation.

Table 6. Test of Endogeneity

OLS-CFE PPML-CFE OLS-CPFE

L.lfdi1 0.073***

(0.022)

L.lprojects1 0.064** (0.026)

L.ljobs1 0.058**

(0.023)

( ) 0.961*** (0.293)

0.477*** (0.084)

0.963*** (0.349)

0.390

(0.381)

0.177

(0.133)

0.745

(0.459)

-0.612

(0.430)

0.039

(0.099)

-0.465

(0.436)

( ) 0.083 (0.225)

-0.005

(0.070)

0.201

(0.256)

-0.435

(0.297)

-0.140*

(0.072)

-0.529

(0.341)

( ) 0.087***

(0.030)

0.026***

(0.007)

0.096***

(0.034)

( ) 0.061*

(0.036)

0.022**

(0.010)

0.069*

(0.041)

Observations 5862 5862 5862

Robust standard errors in parentheses

* p < 0.10, ** p < 0.05, *** p < 0.01

(dep. Variable log(X+1))

7. Conclusions

The interplay among immigration, capital and trade is essential to understand the way

globalization affects economies (Freeman, 2006). The increasing importance of foreign

workers into the labor markets either as employees or business people (entrepreneurs)

has recently raised the question about their influence on foreign investment. This paper

provides new insights into both the migration-FDI link as well as about the impact of

financial constraints faced by foreign investors after the 2007 financial crisis.

Migrants normally have knowledge and experience about their home markets that can

provide valuable information for foreign investors in their countries. Our results suggest

that migration creates a positive externality for both the sending as well as the receiving

countries. Benefits for receiving countries originate because immigration creates

opportunities for new investment projects. Benefits for sending countries are related to

17

the new investment projects received by their native countries which may be a source of

capital accumulation and technology diffusion.

Apart from their influence on FDI flows, migrant networks are likely to reduce financial

constraints faced by foreign firms. This effect manifests itself more pronounced in the

existence of new investment projects which likely would not be approved otherwise.

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